Document Type : Research Paper

Authors

1 School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran.

2 School of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran.

3 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

10.22105/riej.2021.247705.1143

Abstract

Data Envelopment Analysis (DEA) is one of the non-parametric methods for evaluating each unit's efficiency. Limited resources in the healthcare system are the main reason for measuring the efficiency of hospitals. Because Operating Rooms (OR) are the most vital part of any hospital, we determine the factors affecting operating rooms' efficiency and evaluate the performance and ranking of operating rooms in 10 of Tehran's largest hospitals. This model's inputs include accuracy in scheduling surgeries, average turnover time, number of successful surgeries and live patients, number of canceled surgeries, number of surgical errors, and number of emergency surgery. Also, outputs consist of the number of operating rooms and equipment, the average number of beds, the number of employees, and the patient satisfaction rate. First, we determine the weight of inputs and outputs by Group Analytic Hierarchy Process (GAHP) with considering experts' ideas in 10 hospitals; then, we utilize three types of DEA model which are input-oriented CCR (CCR-I), output-oriented CCR (CCR-O), input-output oriented CCR (CCR_IO) and AP models to estimate the efficiency of ORs and rank them.

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Main Subjects

[1]     Roland, B., Di Martinelly, C., Riane, F., & Pochet, Y. (2010). Scheduling an operating theatre under human resource constraints. Computers and industrial engineering58(2), 212-220.
[2]     Hans, E. W., & Nieberg, T. (2007). Operating room manager game. INFORMS transactions on education8(1), 25-36.
[3]     Rezaee, M. J., & Karimdadi, A. (2015). Do geographical locations affect in hospitals performance? A multi-group data envelopment analysis. Journal of medical systems39(9). https://doi.org/10.1007/s10916-015-0278-3
[4]     Chowdhury, H., & Zelenyuk, V. (2016). Performance of hospital services in Ontario: DEA with truncated regression approach. Omega63, 111-122.
[5]     Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research2(6), 429-444.
[6]     Firsova, A. A., & Chernyshova, G. Y. (2019). Mathematical models for evaluation of the higher education system functions with DEA approach. Izv. Saratov Univ. Math. Mech.Inform19(3), 351-362. DOI: https://doi.org/10.18500/1816-9791-2019-19-3-351-362
[7]     Taeb, Z., Hosseinzadeh Lotfi, F., & Abbasbandy, S. (2017). Determine the efficiency of time depended units by using data envelopment analysis. International journal of research in industrial engineering6(3), 193-201.
[8]     Sebt, M. V., Juybari, M. N., & Soleymanfar, V. R. (2018). Investment projects ranking with DEA method considering feasibility study results. International journal of research in industrial engineering7(3), 320-335.
[9]     O'neill, L. (1998). Multifactor efficiency in data envelopment analysis with an application to urban hospitals. Health care management science1(1), 19-27.
[10] Huang, L. J., & Hu, T. Z. (2006). Study of agricultural production efficiency in China's Western region based on dea method [J]. Research of agricultural modernization6. Retrieved from https://en.cnki.com.cn/Article_en/CJFDTotal-NXDH200606005.htm
[11] Chang, Y. T., Zhang, N., Danao, D., & Zhang, N. (2013). Environmental efficiency analysis of transportation system in China: A non-radial DEA approach. Energy policy58, 277-283.
[12] Martí, L., Martín, J. C., & Puertas, R. (2017). A DEA-logistics performance index. Journal of applied economics20(1), 169-192.
[13] Tavakoli, M. M., Molavi, B., & Shirouyehzad, H. (2017). Organizational performance evaluation considering human capital management approach by fuzzy-dea: a case study. International journal of research in industrial engineering6(1), 1-16.
[14] Butler, T. W., & Li, L. (2005). The utility of returns to scale in DEA programming: an analysis of Michigan rural hospitals. European journal of operational research161(2), 469-477.
[15] Burgess Jr, J. F., & Wilson, P. W. (1996). Hospital ownership and technical inefficiency. Management science42(1), 110-123.
[16] Ozcan, Y. A., Merwin, E., Lee, K., & Morrissey, J. P. (2005). Benchmarking using DEA: the case of mental health organizations. In Operations research and health care (pp. 169-189). Boston, MA: Springer.
[17] Min, A., & Scott, L. D. (2016). Evaluating nursing hours per patient day as a nurse staffing measure. Journal of nursing management24(4), 439-448.
[18] Ketabi, S. (2011). Efficiency measurement of cardiac care units of Isfahan hospitals in Iran. Journal of medical systems35(2), 143-150.
[19] Hatefi, S. M., & Haeri, A. (2019). Evaluating hospital performance using an integrated balanced scorecard and fuzzy data envelopment analysis. Journal of health management and informatics6(2), 66-76.
[20] Li, Y., Lei, X., & Morton, A. (2019). Performance evaluation of nonhomogeneous hospitals: the case of Hong Kong hospitals. Health care management science22(2), 215-228.
[21] Khushalani, J., & Ozcan, Y. A. (2017). Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA). Socio-economic planning sciences60, 15-23.
[22] Zare, H., Tavana, M., Mardani, A., Masoudian, S., & Saraji, M. K. (2019). A hybrid data envelopment analysis and game theory model for performance measurement in healthcare. Health care management science22(3), 475-488.
[23] Omrani, H., Shafaat, K., & Emrouznejad, A. (2018). An integrated fuzzy clustering cooperative game data envelopment analysis model with application in hospital efficiency. Expert systems with applications114, 615-628.
[24] Ozcan, Y. A. (2008). Health care benchmarking and performance evaluation. Springer US.
[25] Liao, H., Mi, X., Yu, Q., & Luo, L. (2019). Hospital performance evaluation by a hesitant fuzzy linguistic best worst method with inconsistency repairing. Journal of cleaner production232, 657-671. https://doi.org/10.1016/j.jclepro.2019.05.308
[26] Wang, X., Luo, H., Qin, X., Feng, J., Gao, H., & Feng, Q. (2016). Evaluation of performance and impacts of maternal and child health hospital services using Data Envelopment Analysis in Guangxi Zhuang Autonomous Region, China: a comparison study among poverty and non-poverty county level hospitals. International journal for equity in health15(1), 1-6.
[27] Fiallos, J., Patrick, J., Michalowski, W., & Farion, K. (2017). Using data envelopment analysis for assessing the performance of pediatric emergency department physicians. Health care management science20(1), 129-140.
[28] Dexter, F., Macario, A., Traub, R. D., Hopwood, M., & Lubarsky, D. A. (1999). An operating room scheduling strategy to maximize the use of operating room block time: computer simulation of patient scheduling and survey of patients' preferences for surgical waiting time. Anesthesia and Analgesia89(1), 7-20.
[29] Hamid, M., Nasiri, M. M., Werner, F., Sheikhahmadi, F., & Zhalechian, M. (2019). Operating room scheduling by considering the decision-making styles of surgical team members: a comprehensive approach. Computers and operations research108, 166-181.
[30] Guido, R., & Conforti, D. (2017). A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem. Computers and operations research87, 270-282.
[31] Dios, M., Molina-Pariente, J. M., Fernandez-Viagas, V., Andrade-Pineda, J. L., & Framinan, J. M. (2015). A decision support system for operating room scheduling. Computers and industrial engineering88, 430-443.
[32] Koppka, L., Wiesche, L., Schacht, M., & Werners, B. (2018). Optimal distribution of operating hours over operating rooms using probabilities. European journal of operational research267(3), 1156-1171.
[33] Roshanaei, V., Luong, C., Aleman, D. M., & Urbach, D. (2017). Propagating logic-based Benders’ decomposition approaches for distributed operating room scheduling. European journal of operational research257(2), 439-455.
[34] Ferrari, L. R., Micheli, A., Whiteley, C., Chazaro, R., & Zurakowski, D. (2012). Criteria for assessing operating room utilization in a free‐standing children’s hospital. Pediatric anesthesia22(7), 696-706.
[35] Joustra, P. E., de Wit, J., Van Dijk, N. M., & Bakker, P. J. (2011). How to juggle priorities? An interactive tool to provide quantitative support for strategic patient-mix decisions: an ophthalmology case. Health care management science14(4), 348-360.
[36] Basson, M. D., & Butler, T. (2006). Evaluation of operating room suite efficiency in the Veterans Health Administration system by using data-envelopment analysis. The american journal of surgery192(5), 649-656.
[37] Tyler, D. C., Pasquariello, C. A., & Chen, C. H. (2003). Determining optimum operating room utilization. Anesthesia and analgesia96(4), 1114-1121.
[38] McIntosh, C., Dexter, F., & Epstein, R. H. (2006). The impact of service-specific staffing, case scheduling, turnovers, and first-case starts on anesthesia group and operating room productivity: a tutorial using data from an Australian hospital. Anesthesia and analgesia103(6), 1499-1516.
[39] Hamid, M., Hamid, M., Nasiri, M. M., & Ebrahimnia, M. (2018). Improvement of operating room performance using a multi-objective mathematical model and data envelopment analysis: A case study. International journal of industrial engineering & production research29(2), 117-132.
[40] Charnes, A., Cooper, W., Lewin, A. Y., & Seiford, L. M. (1997). Data envelopment analysis theory, methodology and applications. Journal of the operational research society48(3), 332-333.
[41] Charnes, A., & Cooper, W. W. (1973). An explicit general solution in linear fractional programming. Naval research logistics quarterly20(3), 449-467.
[42] Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science39(10), 1261-1264. https://doi.org/10.1287/mnsc.39.10.1261